A Study on ACCC Surface Defect Classification Method Using ResNet18 with Integrated SE Attention Mechanism

Surface defect detection in aluminum-based composite core conductors (ACCC) via X-ray imaging has long been constrained by challenges such as small sample sizes, class imbalance, model redundancy, and inadequate adaptation to single-channel industrial images. To address this, this paper proposes SE-ResNet18, a lightweight classification model synergistically designed for industrial single-channel X-ray images. The model features a co-adapted architecture where a single-channel input layer (preserving native image information and eliminating RGB conversion overhead) is coupled with a channel attention mechanism (to amplify subtle defect features), all within a globally optimized lightweight framework. With targeted data augmentation and robust training strategies, the model achieves superior performance on the ACCC defect dataset: classification accuracy reaches 98.39%, while excelling in lightweight design (12.0 million parameters) and real-time capability (0.44 ms/image inference speed). The experiments demonstrate that the proposed model exhibits high classification accuracy in testing while offering superior lightweight characteristics and inference efficiency. This provides a feasible solution for achieving high-precision detection and real-time processing in industrial scenarios, showcasing potential for ACCC online detection applications.